etienne87

from __future__ import print_function
import numpy as np
import imutils
import cv2

"""
Homecooked RANSAC, we will see if we manage to replace cv2.findHomography(..., cv2.RANSAC)
"""
def ransac(data, model, n, k, t, d):
    bestfit = None

etienne87

# pylint: disable-all
import torch.nn as nn
from torch.nn import functional as F
import torch

def time_to_batch(x):
    t, n = x.size()[:2]
    x = x.view(n * t, *x.size()[2:])
    return x, n

etienne87

# pylint: disable-all
import torch.nn as nn
from torch.nn import functional as F
import torch

def time_to_batch(x):
    t, n = x.size()[:2]
    x = x.view(n * t, *x.size()[2:])
    return x, n

etienne87

import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import time


def timeit(x, func, iter=10):
	torch.cuda.synchronize()
	start = time.time()

etienne87

from __future__ import division
import time
import numpy as np

class Timing:
    def __init__(self):
        self.total = 0
        self.ncalls = 0
        self.nevents = 0
        self.last_num = -1

etienne87

#!/usr/bin/python
# This script runs a random 3d animation & can retrieve:
# - detection
# - flow
# - zbuffer
#
# Copyright: (c) 2017-2018 Chronocam

import numpy as np
import cv2

etienne87

# pylint: disable-all
from __future__ import print_function
import torch
import random
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from box import box_iou, box_clamp
import numpy as np

etienne87

from __future__ import print_function
import glob
import sys
import time
import multiprocessing as mp, numpy as np, random
from prophesee_utils.td_video import ChronoVideo
import prophesee_utils.td_processing as tdp
import prophesee_utils.vis_utils as vis
import cv2
from numba import njit as jit

etienne87

    #!/usr/bin/python
# torch_bilateral: bi/trilateral filtering in tf
# Copyright: (c) 2017-2018 Prophesee
from __future__ import print_function
import tensorflow as tf
import numpy as np
import pdb
import time

etienne87

#!/usr/bin/python
# torch_bilateral: bi/trilateral filtering in torch
import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn import Parameter
import numpy as np
import pdb
import time